25 citations · 63 across the 11 of their papers we have counts for
13 papers
Same Target, Different Origins: Aligning Latent Rollout for Consistent PDE Emulation
Xinying Li, Jing Xu, Wenjun Wang +4
Neural PDE solvers commonly generate multi-step predictions through autoregressive rollout, recursively applying a learned evolution operator and feeding each prediction back as in…
Degeneracy graph determinants and higher-dimensional Young diagrams
Sanjaye Ramgoolam, Michał Studziński
Kemp and Ramgoolam proposed a basis of monomials for a commutative semisimple algebra with generators \(C_1,\ldots, C_L\), constructed from a degeneracy graph which records how the…
Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection
Xiao Jin, Xin-Yue Mu, Jing Xu
As the saying goes, "seeing is believing". However, with the development of digital face editing tools, we can no longer trust what we can see. Although face forgery detection has…
MUSTER: A Multi-scale Transformer-based Decoder for Semantic Segmentation
Jing Xu, Wentao Shi, Pan Gao +2
In recent works on semantic segmentation, there has been a significant focus on designing and integrating transformer-based encoders. However, less attention has been given to tran…
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
Jing Xu, Xu Luo, Xinglin Pan +3
Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning metho…
SSformer: A Lightweight Transformer for Semantic Segmentation
Wentao Shi, Jing Xu, Pan Gao
It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks. Nevertheless, the original Vision Transformer may lack of i…
Channel Importance Matters in Few-Shot Image Classification
Xu Luo, Jing Xu, Zenglin Xu
Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new classification tasks with a shift in task distribution. Understanding the difficulties posed by this ta…
Exploring Category-correlated Feature for Few-shot Image Classification
Jing Xu, Xinglin Pan, Xu Luo +2
Few-shot classification aims to adapt classifiers to novel classes with a few training samples. However, the insufficiency of training data may cause a biased estimation of feature…
AFINet: Attentive Feature Integration Networks for Image Classification
Xinglin Pan, Jing Xu, Yu Pan +4
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Recent advanced models in CNNs, such as ResNets,…
RegNet: Self-Regulated Network for Image Classification
Jing Xu, Yu Pan, Xinglin Pan +3
The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple sho…
MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance
Jing Xu, Tszhang Guo, Yong Xu +2
Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently. Existing methods have achieved good performance on…
Angular Triplet Loss-based Camera Network for ReID
Yitian Li, Ruini Xue, Mengmeng Zhu +2
Person re-identification (ReID) is a challenging crosscamera retrieval task to identify pedestrians. Many complex network structures are proposed recently and many of them concentr…
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Yu Pan, Jing Xu, Maolin Wang +4
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in…